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Summary
| Description |
English: At initialization, an ensemble of wide neural networks is a zero-mean Gaussian process; during training (gradient descent on mean-square error), the ensemble evolves according to the neural tangent kernel. The converged ensemble is a Gaussian process whose mean is the ridgeless kernel regression estimator and whose variance vanishes on the training points. |
| Date | |
| Source | Own work |
| Author | Dkarkada |
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File history
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 17:15, 8 May 2023 | | 900 × 600 (3.96 MB) | Dkarkada | change xaxis label |
| 05:23, 3 May 2023 |  | 900 × 600 (3.94 MB) | Dkarkada | clarified NTK curve in the legend | |
| 05:14, 3 May 2023 |  | 900 × 600 (3.74 MB) | Dkarkada | Uploaded while editing "Neural tangent kernel" on en.wikipedia.org |
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